If you’re a platform engineering leader managing Kubernetes at scale, a new pressure has entered the room. The business wants AI workloads running on your clusters. GPU nodes. Model inference. Agentic pipelines with zero tolerance for unpredictability. And it wants them yesterday.
The problem? Most Kubernetes environments were never built for this level of determinism. Infrastructure drift has been slowly accumulating for years: mismatched kernels, snowflake clusters, and manual patching cycles that engineers absorb through sheer…

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